https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/Head https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/assertion https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/provenance https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/pubinfo https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/assertion https://doi.org/10.3390/rs18091440 http://purl.org/dc/terms/creator https://orcid.org/0000-0001-8884-9743 https://doi.org/10.3390/rs18091440 http://purl.org/dc/terms/creator https://orcid.org/0000-0002-4135-7634 https://doi.org/10.3390/rs18091440 http://purl.org/dc/terms/creator https://orcid.org/0009-0001-1115-9741 https://doi.org/10.3390/rs18091440 http://purl.org/dc/terms/publisher https://ror.org/00ckv2g77 https://doi.org/10.3390/rs18091440 http://purl.org/dc/terms/subject http://purl.obolibrary.org/obo/NCIT_C19349 https://doi.org/10.3390/rs18091440 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/ff/terms/article https://doi.org/10.3390/rs18091440 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fdof/ontology#FAIRDigitalObject https://doi.org/10.3390/rs18091440 http://www.w3.org/2000/01/rdf-schema#comment Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University and others provide controlled environments for algorithms’ evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of five dimensionality reduction strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)-based selector together with a clustering based baseline, K-Means Clustering-Based Band Selection (KMCBS). These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing. Keywords: hyperspectral imaging; band selection; machine learning; prescribed fire https://doi.org/10.3390/rs18091440 http://www.w3.org/2000/01/rdf-schema#label Hyperspectral Band Selection for Ground Fuel Classification for Prescribed Fires https://doi.org/10.3390/rs18091440 https://schema.org/funder https://ror.org/021nxhr62 https://doi.org/10.3390/rs18091440 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI https://doi.org/10.3390/rs18091440 https://www.w3.org/ns/dcat#contactPoint mahmadisaq.karankot@student.montana.edu https://doi.org/10.3390/rs18091440 https://www.w3.org/ns/dcat#endDate 6 May 2026 https://doi.org/10.3390/rs18091440 https://www.w3.org/ns/dcat#startDate 2024 https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/provenance https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://purl.org/dc/terms/created 2026-06-10T23:35:09.523Z https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://purl.org/nanopub/x/introduces https://doi.org/10.3390/rs18091440 https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RACJ58Gvyn91LqCKIO9zu1eijDQIeEff28iyDrJgjSJF8 https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/sig http://purl.org/nanopub/x/hasSignature K/xYA1SrC52nHlzvkEuSJoe/3GyLE1rb48fam3J4Kajpzf0KVXB5DNi7KZixRV7JkNfvD44a0evbRqFjPHWAXVUVFuUv12ZTmCmgAqJK126aQeOuyfsc2f8Vn8rBwiny3LhLsEWI3roxBndXPHxL1sMjAg5YM+vvEc33zmFN7mYba8UgRYLdfSIksgkwJpYpOaTFrW531tDE6UfYZrDVz2VxrIQzzufJt8nFeBL2sFSogHG0PQH2aeDjWAoyolKa07DD28dFa2O1zSftkSNUtyohZXL6C5w9EcQXh6DALPEa1FIju3vQvpZC58zbe6j8VrsrXVCgzF2KGnoCgjys4Q== https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI https://w3id.org/np/RAIlWyvSpHgeJ45EfmlzgpnZFpplU8VoKjw22guav7JsI/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742